Objective To investigate the status of life quality in the elderly in Shanxi Province Changzhi City, and to analyze its influencing factors. Methods The quality of life, activities of daily living and status of community services were measured simultaneously by 36-Item Short Form Health Survey (SF-36). Activities of Daily Living Scale, Community Healthy Needs Scale and survey results were used and analyzed using t-test, correlation and stepwise regression analysis. Results According to the criterion of life quality in the elderly which were proposed by Zhang Lei, the quality of life in the elderly scored 72. 1-117.0, reached 98.5%. The quality of life in the elderly was impacted by the ability of caring oneself, marital status, degree of culture, economic situation and so on. The demand rate for health guidance and periodic physical examination was higher. Conclusions The quality of life in the elderly is at a medium level. There is a wide gap between the demand of community healthy services and the utilization of community healthy services.

Objective To investigate the quality of life of elderly caregivers and its factors. Methods Elderly caregivers living in Changzhi City were enrolled in this study. 36-item short-form (SF-36) health survey and Activity of Daily Living Scale ( ADL) were used as study tools. The data was analyzed by statistical description,t test or multiple regression. Results Those with score of quality of life reaching 72. 1 to 117. 0 accounted for 98. 8%. The total score and score of eight dimensions of SF-36 showed statistically significant difference between different daily living activity groups ( P < 0.05) . The state of health and self-care ability of the elderly imposed a major impact on eight dimensions of SF-36. Age, sex, level of education, marital status and wage had different impact on each dimension of SF-36. Conclusion The quality of life of elderly caregivers is at a medium level. The main factors of quality of live of elderly caregiver are their state of health and self-care ability of the elderly.

How to detect the P300 component in EEG accurately and instantly is a hot problem in the research field of Brain-Computer Interface. In this paper, an algorithm based on F-score feature selection and support vector machines was introduced for P300 detection. Using F-score feature selection method, we reduced input features to overcome the shortcoming of support vector machines in terms of low detection speed, and then implemented the detection of P300 component with support vector machines, which have good classification performance. The algorithm was tested with a P300 dataset from the BCI competition 2003. The results showed that the algorithm achieved an accuracy of 100% in P300 detection within five repetitions, and the detection speed of this algorithm was 2 times higher than that of the traditional support vector machines algorithm without F-score feature selection.
Algorithms ; Electroencephalography ; methods ; Event-Related Potentials, P300 ; physiology ; Humans ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted

Objective To investigate the effect of long non-coding RNA(lncRNA)lung cancer-related transcript 1(LUCAT1)on the pathogenesis of intrauterine adhesion by regulating the expression of amphiregulin(AREG),to provide a new molecular target for the prevention and treatment of intrauterine adhesions.Methods Real-time quantitative polymerase chain reaction(RTqPCR)was used to determine the mRNA expression levels of lncRNA LUCAT1,fibrotic markers of α-smooth muscle actin(α-SMA)and collagen type Ⅰ alpha 1 chain protein(COL1A1)in samples of intrauterine adhesion tissue and endometrial stromal cell treated with transforming growth factor-β1.Western blot was used to determine the protein expression levels of AREG,α-SMA,COL1A1.Then,si-LUCAT1,pcDNA LUCAT1,si-AREG were transfected into ESC and RT-qPCR was used to detect the mRNA expression levels of lncRNA LUC-AT1 and AREG.Next,si-LUCAT1 and pcDNA LUCAT1 were transfected into ESC and treated these cells with TGF-β1 for 48h,re-spectively.Western blot was used to further detect the protein expression levels of AREG,α-SMA and COL1A1,and cell proliferation and apoptosis were detected by cell proliferation assay and cell apoptosis assay.Results The expression levels of lncRNA LUC AT1,AR-EG and fibrosis markers α-SMA and COL1A1 were upregulated in endometrial tissues from patients with intrauterine adhesion and in ESC that had been treated with TGF-β1.AREG changed with the change of lncRNA LUCAT1,and the expression of lncRNA LUC-AT1did not change significantly after downregulation of AREG,and AREG was positively regulated by lncRNA LUCAT1.During the process of the transformation of ESC into fibroblasts,si-LUCAT1 significantly inhibited the protein expression levels of AREG,α-SMA and COL1A1(P＜0.01),significantly reduced cell proliferation and significantly induced cell apoptosis,the difference was statistically significant(P＜0.001).pcDNA LUCAT1 significantly induced the protein expression levels of AREG,α-SMA and COL1A1,and the difference was statistically significant(P＜0.01).Conclusion LncRNA LUCAT1 promotes the pathogenesis of intrauterine adhesion by up-regulating the expression of AREG.LncRNA LUCAT1/AREG axis may provide novel molecular target for the prevention and treat-ment of intrauterine adhesion.

This study aims to explore the role of mouse GSDME in tumor progression and its mechanisms.Wide type and GSDME knockout mice were used to establish tumor models and the tumor growth was monitored;CRISPR-Cas9 was employed to knockout GSDME in MC38 cells.Bone marrow-derived macrophage and dendritic cells were culture in vitro and adoptively transferred into tumor-bearing mice,respectively.Western blotting was conducted to detect protein expression in cells;flow cytometry was used to analysis the immune cell number,CD8+T cell function,and Cleaved Caspase-3 expression in TAM and TADC cells.According to the above experiments,we found that GSDME deficiency promoted tumor progression in mice.GSDME knockout reduced the number of CD8+T cells and decreased IFN-γ and Perforin in CD8+T cells in tumors.GSDME was mainly expressed in macrophages and dendritic cells,and adoptive transferring GSDME deficient macrophages or dendritic cells did not affect tumor progression.However,GSDME deficient mice reduced the ratio of Cleaved Caspase-3 positive macrophages and dendritic cells in tumors,and Cleaved Caspase-3 positive macrophages expressed more iNOS and MHCII,while Cleaved Caspase-3 positive dendritic cells expressed more CD80 and CD86.In general,GSDME increases Cleaved Caspase-3 positive macrophages and dendritic cells that promote CD8+T cell function,thereby inhibiting tumor progression.

OBJECTIVES: Patients from different social environments and cultural backgrounds have different nursing needs. If nurses ignore the cultural differences of patients, it is easy to lead to the strained nurse-patient relationship, affect the nursing effect and cause harm to patients. Critical cultural competence (CCC) can help nurses to meet the nursing needs of patients from different cultural backgrounds, which is beneficial to building a harmonious nurse-patient relationship and improving the quality of nursing. Almutairi, et al designed the Critical Cultural Competence Scale (CCCS) which can be used to evaluate accurately nurses' CCC. No studies have reported the development of a critical cultural competence measurement tool for nurses or the introduction of foreign scales in China. This study aims to conduct Chinese and cross-cultural debugging and test the reliability of the English version of the CCCS in order to form CCCS suitable for Chinese cultural background and provide an effective evaluation tool for investigating the current situation of clinical nurses' CCC. METHODS: This study used Brislin's back-translation model to translate and back-translation the English version of CCCS. The Chinese version of CCCS was then created through cross-cultural debugging by expert consultation and a pre-survey with a sample size of 30 clinical nurses. From August to October 2019, 580 clinical nurses were surveyed using a whole group sampling method. The participants were randomly divided into 2 groups with a 7꞉3 ratio. One group (n=406) was used for exploratory factor analysis and reliability analysis, while the other group (n=174) was used for confirmatory factor analysis. Six experts used the scale-level content validity index (S-CVI) and the item-level content validity index (I-CVI) to assess content validity. In the exploratory factor analysis, items were screened using the critical ratio method, and were tested using the KMO (Kalser-Meyer-Olkin) index, Bartlett's sphericity test, and principal component analysis. In the confirmatory factor analysis, average variance extracted (AVE), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), and root mean square error of approximation (RMSEA) were used to assess the degree of fit of the constructed model. For the total scale and the 4 subscales, the Cronbach's α coefficient, split-half reliability, and retest reliability were used to assess the scale's reliability. RESULTS: The S-CVI was 0.930, while the I-CVI ranged from 0.833 to 0.944. For all items, the critical ratio exceeded 3, and the difference between the high and low subgroups was statistically significant (P<0.05). Exploratory factor analysis revealed critical knowledge subscale had a KMO value of 0.676, with the total scale and other 3 subscales all having a KMO value >0.8 and a chi-square value of 814.32 to 12 442.45 for the Bartlett's spherical test, with degree of freedom ranging from 21 to 136 (P<0.001), indicating that all items were suitable for factor analysis. The principal component analysis showed that 17, 12, 7, and 7 items were extracted from the 4 subscales, with 4, 3, 2, and 2 components whose eigenvalues were more than 1, and the cumulative variance contribution was 66.0%, 54.3%, 56.6%, and 70.2%, respectively. The confirmatory factor analysis showed that the AVE of the 4 subscales were 0.637, 0.499, 0.560, and 0.565, GFI was 0.904, AGFI was 0.863, and RMSEA was 0.076. The Cronbach's α coefficient for the total scale and subscales ranged from 0.811 to 0.878, the split-half reliability ranged from 0.707 to 0.842, and the retest reliability was 0.827. CONCLUSIONS The Chinese version of the CCCS has good reliability and validity, and it can be used as a valid assessment tool for clinical nurses' critical cultural competence in China.
Humans ; Cultural Competency ; Reproducibility of Results ; Psychometrics/methods* ; Factor Analysis, Statistical ; China